Wa-LiD: A New LiDAR Simulator for Waters

Abdallah, Hani; Baghdadi, Nicolas; Bailly, Jean-Stéphane; Pastol, Yves; Fabre, Frédéric

doi:10.1109/lgrs.2011.2180506

Cited by 86 publications

(64 citation statements)

References 19 publications

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“…An analogous forward model does not exist for bathymetric LiDAR, although waveform simulation algorithms could serve as a starting point (Abdallah et al. ; Bouhdaoui et al. ).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the capabilities of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR for measuring channel morphology in two distinct river environments

Legleiter

Overstreet

Glennie

et al. 2015

Earth Surf Processes Landf

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Remote sensing is a powerful tool for examining river morphology. This study used detailed field surveys to assess the capability of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR to measure bed elevations in rivers with disparate optical characteristics. Field measurements of water column optical properties in the clear Snake River, the more complex Blue and Colorado, and highly turbid Muddy Creek were used to calculate depth retrieval precision and dynamic range. Differences in depth of a few centimeters were detectable via passive optical techniques in the clearest stream, but precision was greatly reduced under turbid conditions. The bathymetric LiDAR evaluated in this study could not detect shallow depths or differences in depth smaller than 11 cm owing to the difficulty of distinguishing water surface and bottom returns in laser waveforms. In clear water and with high radiometric resolution, hyperspectral systems such as CASI could detect depths approaching 10 m, but semi-empirical analysis of the Aquarius LiDAR indicated that maximum detectable depths were of the order of 2-3 m in the clear-flowing Snake River, and closer to 1 m in the more turbid streams. Turbidity also constrained spectrally based depth retrieval, and depth estimates from the Blue/Colorado were far less reliable than on the Snake. Both sensors yielded positively biased (0.03 m for CASI, 0.08 m for Aquarius) bed elevations on the Snake, with precisions of 0.16-0.17 m. For the Blue/Colorado, mean errors were of the order of 0.2 m, biased shallow for optical data and biased deep for LiDAR, although no Aquarius laser returns were recorded from the deepest parts of these channels; precisions were reduced to 0.29-0.32 m. Both approaches have advantages and limitations, and prospective users must understand the capabilities and constraints associated with various types of remote sensing to ensure efficient use of these evolving technologies.

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“…An analogous forward model does not exist for bathymetric LiDAR, although waveform simulation algorithms could serve as a starting point (Abdallah et al. ; Bouhdaoui et al. ).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the capabilities of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR for measuring channel morphology in two distinct river environments

Legleiter

Overstreet

Glennie

et al. 2015

Earth Surf Processes Landf

View full text Add to dashboard Cite

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“…Abady et al [32] replaced the triangle function with a quadrilateral function to fit volume backscatter returns and assessed the method by using waveforms simulated by a Water LiDAR (Wa-LID) waveform simulator. Wa-LID was developed to simulate the reflection of LiDAR waveforms from water across visible wavelengths [33]. Although the accuracy of quadrilateral function is slightly better than that of the triangle function [32], the number of parameters used in the former (six parameters) is more than that used in the latter (four parameters) and thus increase the complexity of waveform decomposition.…”

Section: Waveform Decompositionmentioning

confidence: 99%

Remote Sensing of Suspended Sediment Concentrations Based on the Waveform Decomposition of Airborne LiDAR Bathymetry

Zhao

Zhang

et al. 2018

Remote Sensing

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Airborne LiDAR bathymetry (ALB) has been shown to have the ability to retrieve water turbidity using the waveform parameters (i.e., slopes and amplitudes) of volume backscatter returns. However, directly and accurately extracting the parameters of volume backscatter returns from raw green-pulse waveforms in shallow waters is difficult because of the short waveform. This study proposes a new accurate and efficient method for the remote sensing of suspended sediment concentrations (SSCs) in shallow waters based on the waveform decomposition of ALB. The proposed method approaches raw ALB green-pulse waveforms through a synthetic waveform model that comprises a Gaussian function (for fitting the air-water interface returns), triangle function (for fitting the volume backscatter returns), and Weibull function (for fitting the bottom returns). Moreover, the volume backscatter returns are separated from the raw green-pulse waveforms by the triangle function. The separated volume backscatter returns are used as bases to calculate the waveform parameters (i.e., slopes and amplitudes). These waveform parameters and the measured SSCs are used to build two power SSC models (i.e., SSC (C)-Slope (K) and SSC (C)-Amplitude (A) models) at the measured SSC stations. Thereafter, the combined model is formed by the two established C-K and C-A models to retrieve SSCs. SSCs in the modeling water area are retrieved using the combined model. A complete process for retrieving SSCs using the proposed method is provided. The proposed method was applied to retrieve SSCs from an actual ALB measurement performed using the Optech Coastal Zone Mapping and Imaging LiDAR in a shallow and turbid water area. A mean bias of 0.05 mg/L and standard deviation of 3.8 mg/L were obtained in the experimental area using the combined model.

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“…The model used in the constrained optimization is designed from the perspective of simple optimization for information extraction purposes and not from the physical standpoint. The model is a modified version of the LiDAR simulator described in [19]. The ortho-MADS direct search algorithm implemented as a part of the NOMAD [20] software package was used [21].…”

Section: Waveform Modeling and Feature Extractionmentioning

confidence: 99%

Data-Driven Approach to Benthic Cover Type Classification Using Bathymetric LiDAR Waveform Analysis

et al. 2015

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A data-driven method for describing the benthic cover type based on full-waveform bathymetric LiDAR data analysis is presented. The waveform of the bathymetric LiDAR return pulse is first modeled as a sum of three functions: a Gaussian pulse representing the surface return, a function modeling the backscatter and another Gaussian pulse modeling the return from the bottom surface. Two sets of variables are formed: one containing features describing the bottom return and the other describing various conditions, such as water quality and the depth of the seabed. Regression analysis is used to eliminate the effect of the condition variables on the features, after which the features are mapped onto a cell lattice using a self-organizing map (SOM). The cells of the SOM are grouped into seven clusters using the neighborhood distance matrix method. The clustering result is evaluated using the seabed substrate map based on sonar measurements, as well as delineation of photic zones in the study area. High correspondence between the clusters and the substrate type/photic zone has been obtained indicating that the proposed clustering method adequately describes the benthic cover in the study area. The bottom return pulse waveforms corresponding to the clusters and a cluster map of the study area are also presented. The method can be used for clustering full waveform bathymetric LiDAR data acquired from large areas to discover the structure of benthic cover types and to focus the field studies accordingly.Remote Sens. 2015, 7 13391

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Wa-LiD: A New LiDAR Simulator for Waters

Cited by 86 publications

References 19 publications

Evaluating the capabilities of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR for measuring channel morphology in two distinct river environments

Evaluating the capabilities of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR for measuring channel morphology in two distinct river environments

Remote Sensing of Suspended Sediment Concentrations Based on the Waveform Decomposition of Airborne LiDAR Bathymetry

Data-Driven Approach to Benthic Cover Type Classification Using Bathymetric LiDAR Waveform Analysis

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